Image Classification Technique using Modified Particle Swarm Optimization

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dc.contributor.author Shukran, MA
dc.contributor.author Chung, YY
dc.contributor.author Yeh, W
dc.contributor.author Wahid, N
dc.contributor.author Zaidi, AM
dc.date.accessioned 2012-10-12T03:33:48Z
dc.date.issued 2011-01
dc.identifier.citation Modern Applied Science, 2011, 5 (5), pp. 150 - 164
dc.identifier.issn 1913-1844
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18297
dc.description.abstract Image classification is becoming ever more important as the amount of available multimedia data increases. With the rapid growth in the number of images, there is an increasing demand for effective and efficient image indexing mechanisms. For large image databases, successful image indexing will greatly improve the efficiency of content based image classification. One attempt to solve the image indexing problem is using image classification to get high-level concepts. In such systems, an image is usually represented by various low-level features, and high-level concepts are learned from these features. PSO has recently attracted growing research interest due to its ability to learn with small samples and to optimize high-dimensional data. Therefore, this paper will introduce the related work on image feature extraction. Then, several techniques of image feature extraction will be introduced which include two main methods. These methods are RGB and Discrete Cosine Transformation (DCT). Finally, several experimental designs and results concerning the application of the proposed image classification using modified PSO classifier will be described in detail.
dc.publisher Canadian Center of Science and Education
dc.subject NA
dc.subject NA
dc.title Image Classification Technique using Modified Particle Swarm Optimization
dc.type Journal Article
dc.description.version Published
dc.parent Modern Applied Science
dc.journal.volume 5
dc.journal.volume 5
dc.journal.number 5 en_US
dc.publocation Canadian en_US
dc.identifier.startpage 150 en_US
dc.identifier.endpage 164 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 0000074095 en_US
dc.personcode 0000065024 en_US
dc.personcode 106463 en_US
dc.personcode 0000074096 en_US
dc.personcode 0000074097 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.description.keywords Social Sciences
dc.description.keywords Science & Technology
dc.description.keywords Life Sciences & Biomedicine
dc.description.keywords Economics
dc.description.keywords Health Care Sciences & Services
dc.description.keywords Health Policy & Services
dc.description.keywords Business & Economics
dc.description.keywords ECONOMICS
dc.description.keywords HEALTH CARE SCIENCES & SERVICES
dc.description.keywords HEALTH POLICY & SERVICES
dc.description.keywords wages
dc.description.keywords body mass index
dc.description.keywords quantile regression
dc.description.keywords endogeneity
dc.description.keywords OCCUPATIONAL ATTAINMENT
dc.description.keywords ECONOMIC CONSEQUENCES
dc.description.keywords YOUNG ADULTHOOD
dc.description.keywords OBESITY
dc.description.keywords ADOLESCENCE
dc.description.keywords EARNINGS
dc.description.keywords STATURE
dc.description.keywords COHORT
dc.description.keywords WEIGHT
dc.description.keywords BEAUTY
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software


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